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Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models

Author

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  • Chia Yean Lim

    (School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-2-Name: Wenchuan Sun Author-2-Workplace-Name: School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-3-Name: Fengqi Guo Author-3-Workplace-Name: CITIC Securities, 150000, Harbin, China Author-4-Name: Sau Loong Ang Author-4-Workplace-Name: Department of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Penang Branch, 11200, Tanjung Bungah, Malaysia Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

" Objective - As the investment environment improves, individuals are increasingly eager to invest their idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on the comprehensive models used by each securities company, including stock market trading, data, and stock pricing models. However, securities companies have not adequately explored a single suitable model for stock predictions and have rarely assessed the effectiveness of stacking and ensemble methods in improving these predictions. Methodology - This research first explored and proposed the best single-stock prediction model. Next, it combined four individual prediction models to create a stacking model. Findings - The comparison between the single and stacking models demonstrated that the stacking model's prediction accuracy exceeded that of the single model. Therefore, it is recommended that securities companies adopt a stacking-type prediction model to forecast share prices for their investment customers. Novelty - Using a stacking model could improve the accuracy of stock price predictions for investment managers, help users make better decisions, and ultimately enhance the company's earnings by delivering more accurate investment outcomes. Type of Paper - Empirical"

Suggested Citation

  • Chia Yean Lim, 2026. "Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models," GATR Journals gjbssr674, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:gjbssr674
    DOI: https://doi.org/10.35609/gjbssr.2026.14.1(2)
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    References listed on IDEAS

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    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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